A Computer Vision Framework For Finger-Tapping Evaluation In Parkinson's Disease

Objective: To define and evaluate a Computer-Vision (CV) method for scoring Paced Finger-Tapping (PFT) in Parkinson's disease (PD) using quantitative motion analysis of index-fingers and to compare the obtained scores to the UPDRS (Unified Parkinson's Disease Rating Scale) finger-taps (FT). Background: The naked-eye evaluation of PFT in clinical practice results in coarse resolution to determine PD status. Besides, sensor mechanisms for PFT evaluation may cause patients discomfort. In order to avoid cost and effort of applying wearable sensors, a CV system for non-invasive PFT evaluation is introduced. Methods: A database of 221 PFT videos from 6 PD patients was processed. The subjects were instructed to position their hands above their shoulders besides the face and tap the index-finger against the thumb consistently with speed. They were facing towards a pivoted camera during recording. The videos were rated by two clinicians between symptom levels 0-to-3 using UPDRS-FT. The CV method incorporates a motion analyzer and a face detector. The method detects the face of testee in each video-frame. The frame is split into two images from face-rectangle center. Two regions of interest are located in each image to detect index-finger motion of left and right hands respectively. The tracking of opening and closing phases of dominant hand index-finger produces a tapping time-series. This time-series is normalized by the face height. The normalization calibrates the amplitude in tapping signal which is affected by the varying distance between camera and subject (farther the camera, lesser the amplitude). A total of 15 features were classified using K-nearest neighbor (KNN) classifier to characterize the symptoms levels in UPDRS-FT. The target ratings provided by the raters were averaged. Results: A 10-fold cross validation in KNN classified 221 videos between 3 symptom levels with 75% accuracy. An area under the receiver operating characteristic curves of 82.6% supports feasibility of the obtained features to replicate clinical assessments. Conclusions: The system is able to track index-finger motion to estimate tapping symptoms in PD. It has certain advantages compared to other technologies (e.g. magnetic sensors, accelerometers etc.) for PFT evaluation to improve and automate the ratings

Motion Cues Analysis for Parkinson Gait Recognition

Background: Previous assessment methods for PG recognition used sensor mechanisms for PG that may cause discomfort. In order to avoid stress of applying wearable sensors, computer vision (CV) based diagnostic systems for PG recognition have been proposed. Main constraints in these methods are the laboratory setup procedures: Novel colored dresses for the patients were specifically designed to segment the test body from a specific colored background. Objective: To develop an image processing tool for home-assessment of Parkinson Gait(PG) by analyzing motion cues extracted during the gait cycles. Methods: The system is based on the idea that a normal body attains equilibrium during the gait by aligning the body posture with the axis of gravity. Due to the rigidity in muscular tone, persons with PD fail to align their bodies with the axis of gravity. The leaned posture of PD patients appears to fall forward. Whereas a normal posture exhibits a constant erect posture throughout the gait. Patients with PD walk with shortened stride angle (less than 15 degrees on average) with high variability in the stride frequency. Whereas a normal gait exhibits a constant stride frequency with an average stride angle of 45 degrees. In order to analyze PG, levodopa-responsive patients and normal controls were videotaped with several gait cycles. First, the test body is segmented in each frame of the gait video based on the pixel contrast from the background to form a silhouette. Next, the center of gravity of this silhouette is calculated. This silhouette is further skeletonized from the video frames to extract the motion cues. Two motion cues were stride frequency based on the cyclic leg motion and the lean frequency based on the angle between the leaned torso tangent and the axis of gravity. The differences in the peaks in stride and lean frequencies between PG and normal gait are calculated using Cosine Similarity measurements. Results: High cosine dissimilarity was observed in the stride and lean frequencies between PG and normal gait. High variations are found in the stride intervals of PG whereas constant stride intervals are found in the normal gait. Conclusions: We propose an algorithm as a source to eliminate laboratory constraints and discomfort during PG analysis. Installing this tool in a home computer with a webcam allows assessment of gait in the home environment.

Viktiga faktorer i egenvårdsprogram som främjar livskvaliteten för personer med diabetes typ 2. : En litteraturöversikt

Bakgrund: Diabetes typ 2 är en endokrin sjukdom och är en av de största folksjukdomarna i världen. Förhöjda blodsockervärden gör att både små och stora blodkärl tar skada och detta leder till olika komplikationer såsom hjärtinfarkt, stroke och njurskador. Med hjälp av viktnedgång, kostreglering, regelbundet fysisk aktivitet och övervakning av blodglukosnivåerna kan risken för komplikationer förebyggas. Genom att förebygga komplikationer kan livskvaliteten främja patientens dagliga liv. En del av diabetesvården består av egenvårdsprogram där patienten får stöd och rådgivning att hantera sin diabetes. Syfte: Denna litteraturstudie syftar till att studera vilka faktorer i egenvårdsprogram som främjar livskvaliteten hos patienter med diabetes typ 2. Metod: Litteraturstudie, artiklarna söktes i databaserna CINAHL, PubMed och Web of Science. 14 kvantitativa artiklar inkluderades. Resultat: Resultatet visade att information, individuell målsättning och uppföljning var viktiga faktorer i egenvårdsprogrammen för att främja livskvaliteten hos patienter med diabetes typ 2. Slutsats: Att leva med diabetes typ 2 kräver noggrannhet och planering i det dagliga livet. Egenvårdsprogram kan minska risken för komplikationer där följsamhet till egenvården främjas och livskvaliteten gynnas.

Motion Detection for Video Surveillance

This thesis is related to the broad subject of automatic motion detection and analysis in videosurveillance image sequence. Besides, proposing the new unique solution, some of the previousalgorithms are evaluated, where some of the approaches are noticeably complementary sometimes.In real time surveillance, detecting and tracking multiple objects and monitoring their activities inboth outdoor and indoor environment are challenging task for the video surveillance system. Inpresence of a good number of real time problems limits scope for this work since the beginning. Theproblems are namely, illumination changes, moving background and shadow detection.An improved background subtraction method has been followed by foreground segmentation, dataevaluation, shadow detection in the scene and finally the motion detection method. The algorithm isapplied on to a number of practical problems to observe whether it leads us to the expected solution.Several experiments are done under different challenging problem environment. Test result showsthat under most of the problematic environment, the proposed algorithm shows the better qualityresult.

Viljan till fysisk aktivitet : En intervention avsedd att stimulera ungdomar att bli fysiskt aktiva

Jenny Isberg (2009): Viljan till fysisk aktivitet – en intervention avsedd att stimulera ungdomar att bli fysiskt aktiva. Örebro Studies in Sport Sciences 6, 141 pp. Physical education (PE) at school may play an important role in the process of becoming physically active in the adolescence and in developing a physically active lifestyle. The opportunities for teachers to provide positive physical activity experiences to the student population extend regularly over the school terms. For some students, PE can be the only opportunity they have to be physically active. Therefore it is important that the students become motivated to practice physical activity and know the purpose with physical activity. The overall purpose of this dissertation was to investigate the possibilities to motivate youth, 12-16 years old, to practice physical activity and hopefully to stimulate them to continue to be physically active in young adulthood. The sample consisted of 122 Swedish compulsory school students (12-16 years old), 51 girls and 71 boys, who were either physically inactive, physically active but not formal members of sport clubs, or physically active and members of a sport club. They used a self-monitoring instrument to describe their physical activity and we compared their self-reported physical activity with their actual VO2 capacity and physical status. The self-monitoring instrument was further validated against an activity monitor, RT3. The intervention lasted one and a half years, and four to five years later a follow-up study was done. Using a quasi-experimental design, the three groups of participants were compared with youths who did not use the self-monitoring instrument. The main findings were that the associations between the accelerometer counts and the activities the students recorded in the self-monitoring instrument were high. The participants  in Group 1 (physically inactive) continued exercising nearly to the same extent as during the intervention while youths in a matched control group did not develop regular physical activity habits. Concerning Group 2 (exercisers), participants in the intervention group were more physically active both during the intervention and at follow-up, compared with a matched control group of exercisers who did not use the self-monitoring instrument. In Group 3 (sport team members), there was no difference between the intervention group and a matched control group after the intervention or at follow-up.  The conclusion of these main findings was that when someone motivates students to continue being physically active and to change their physical activity patterns in a positive direction, the self-monitoring instrument can be a door-opener for youths who are physically inactive or regularly active outside sports clubs.

Solar Energy assessment based on weather station data for direct site monitoring in Indonesia

This thesis evaluates different sites for a weather measurement system and a suitable PV- simulation for University of Surabaya (UBAYA) in Indonesia/Java. The weather station is able to monitor all common weather phenomena including solar insolation. It is planned to use the data for scientific and educational purposes in the renewable energy studies. During evaluation and installation it falls into place that official specifications from global meteorological organizations could not be meet for some sensors caused by the conditions of UBAYA campus. After arranging the hardware the weather at the site was monitored for period of time. A comparison with different official sources from ground based and satellite bases measurements showed differences in wind and solar radiation. In some cases the monthly average solar insolation was deviating 42 % for satellite-based measurements. For the ground based it was less than 10 %. The average wind speed has a difference of 33 % compared to a source, which evaluated the wind power in Surabaya. The wind direction shows instabilities towards east compared with data from local weather station at the airport. PSET has the chance to get some investments to investigate photovoltaic on there own roof. With several simulations a suitable roof direction and the yearly and monthly outputs are shown. With a 7.7 kWpeak PV installation with the latest crystalline technology on the market 8.82 MWh/year could be achieved with weather data from 2012. Thin film technology could increase the value up to 9.13 MWh/year. However, the roofs have enough area to install PV. Finally the low price of electricity in Indonesia makes it not worth to feed in the energy into the public grid.

Automating condition monitoring of vegetation on railway trackbeds and embankments

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment. Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body. A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways. The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process. Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms. The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.